Remembering indigenous dispossession in the national museum: The National Museum of Australia and the Canadian Museum of Civilization
Bibliographic record
Abstract
Recent decades have seen the escalation of debate across western democracies that were once sites of the British Empire about how to remember the history of colonialism. This essay will consider how these debates have manifested in relation to the history of indigenous dispossession and its remembrance in Australia and Canada, which not only share many parallels in their stories of settlement but also in their recent efforts to come to terms with historical injustices against indigenous peoples. In examining how these debates have taken shape in the representation of national history in Australia’s and Canada’s recently established national museums, this essay will question the degree to which public historical consciousness in these former settler societies demonstrates a political imperative to remember historical injustices on the one hand, and on the other hand an enduring desire to forget them in favour of a more unifying story of the nation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.029 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".